Alpha channel prediction

ABSTRACT

Image coding using alpha channel prediction may include generating a reconstructed image using alpha channel prediction and outputting the reconstructed image. Generating the reconstructed image using alpha channel prediction may include decoding reconstructed color channel values for a current pixel expressed with reference to first color space, obtaining color space converted color channel values for the current pixel by converting the reconstructed color channel values to a second color space, obtaining an alpha channel lower bound for an alpha channel value for the current pixel using the color space converted color channel values, generating a candidate predicted alpha value for the current pixel, obtaining an adjusted predicted alpha value for the current pixel using the candidate predicted alpha value and the alpha channel lower bound, generating a reconstructed pixel for the current pixel using the adjusted predicted alpha value, and including the reconstructed pixel in the reconstructed image.

BACKGROUND

Digital images and video can be used, for example, on the internet, forremote business meetings via video conferencing, high definition videoentertainment, video advertisements, or sharing of user-generatedcontent. Due to the large amount of data involved in transferring andprocessing image and video data, high-performance compression may beadvantageous for transmission and storage. Accordingly, it would beadvantageous to provide high-resolution image and video transmitted overcommunications channels having limited bandwidth, such as image andvideo coding using alpha channel prediction.

SUMMARY

This application relates to encoding and decoding of image data, videostream data, or both for transmission or storage. Disclosed herein areaspects of systems, methods, and apparatuses for encoding and decodingusing alpha channel prediction.

An aspect is a method for image coding using alpha channel prediction.Image coding using alpha channel prediction may include generating areconstructed image using alpha channel prediction and outputting thereconstructed image. Generating the reconstructed image using alphachannel prediction may include obtaining reconstructed color channelvalues for a current pixel of the current image expressed with referenceto first color space, obtaining color space converted color channelvalues for the current pixel by converting the reconstructed colorchannel values to a second color space, obtaining an alpha channel lowerbound for an alpha channel value for the current pixel using the colorspace converted color channel values, generating a candidate predictedalpha value for the current pixel, obtaining an adjusted predicted alphavalue for the current pixel using the candidate predicted alpha valueand the alpha channel lower bound, generating a reconstructed pixel forthe current pixel using the adjusted predicted alpha value, andincluding the reconstructed pixel in the reconstructed image.

Another aspect is a method for image coding using alpha channelprediction. Image coding using alpha channel prediction may includegenerating an encoded image using alpha channel prediction andoutputting an output bitstream. Generating the encoded image using alphachannel prediction may include identifying a current pixel from an inputimage, wherein the current pixel includes input color channel values,wherein the input color channel values are expressed with reference tofirst color space, and wherein the input color channel values include aninput alpha channel value, obtaining pre-multiplied color channel valuesfor the pixel using the input color channel values, obtainingreconstructed color values for the pixel using the pre-multiplied colorchannel values, wherein the reconstructed color channel values areexpressed with reference to second color space, obtaining color spaceconverted color channel values for the current pixel by color spaceconverting the reconstructed color channel values to the first colorspace, obtaining an alpha channel lower bound for a reconstructed alphachannel value for the current pixel using the color space convertedcolor channel values, generating a candidate predicted alpha value forthe current pixel, obtaining an adjusted predicted alpha value for thecurrent pixel using the candidate predicted alpha value and the alphachannel lower bound, obtaining a residual alpha value as a difference ofsubtracting the adjusted predicted alpha value from the input alphachannel value, and including the residual alpha value in an outputbitstream.

Another aspect is an apparatus for image coding using alpha channelprediction. The apparatus may include a processor configured to generatea reconstructed image using alpha channel prediction and output thereconstructed image. The processor may be configured to generate thereconstructed image using alpha channel prediction by obtainingreconstructed color channel values for a current pixel of the currentimage expressed with reference to first color space, obtaining colorspace converted color channel values for the current pixel by convertingthe reconstructed color channel values to a second color space,obtaining an alpha channel lower bound for an alpha channel value forthe current pixel using the color space converted color channel values,generating a candidate predicted alpha value for the current pixel,obtaining an adjusted predicted alpha value for the current pixel usingthe candidate predicted alpha value and the alpha channel lower bound,generating a reconstructed pixel for the current pixel using theadjusted predicted alpha value, and including the reconstructed pixel inthe reconstructed image.

Another aspect is an apparatus for image coding using alpha channelprediction. The apparatus may include a processor configured to generatean encoded image using alpha channel prediction and output the encodedimage in an output bitstream. The processor may be configured togenerate the encoded image using alpha channel prediction by identifyinga current pixel from an input image, wherein the current pixel includesinput color channel values, wherein the input color channel values areexpressed with reference to first color space, and wherein the inputcolor channel values include an input alpha channel value, obtainingpre-multiplied color channel values for the pixel using the input colorchannel values, obtaining reconstructed color values for the pixel usingthe pre-multiplied color channel values, wherein the reconstructed colorchannel values are expressed with reference to second color space,obtaining color space converted color channel values for the currentpixel by color space converting the reconstructed color channel valuesto the first color space, obtaining an alpha channel lower bound for areconstructed alpha channel value for the current pixel using the colorspace converted color channel values, generating a candidate predictedalpha value for the current pixel, obtaining an adjusted predicted alphavalue for the current pixel using the candidate predicted alpha valueand the alpha channel lower bound, obtaining a residual alpha value as adifference of subtracting the adjusted predicted alpha value from theinput alpha channel value, and including the residual alpha value in anoutput bitstream.

Variations in these and other aspects will be described in additionaldetail hereafter.

BRIEF DESCRIPTION OF THE DRAWINGS

The description herein makes reference to the accompanying drawingswherein like reference numerals refer to like parts throughout theseveral views unless otherwise noted or otherwise clear from context.

FIG. 1 is a diagram of a computing device in accordance withimplementations of this disclosure.

FIG. 2 is a diagram of a computing and communications system inaccordance with implementations of this disclosure.

FIG. 3 is a diagram of a video stream for use in encoding and decodingin accordance with implementations of this disclosure.

FIG. 4 is a block diagram of an encoder in accordance withimplementations of this disclosure.

FIG. 5 is a block diagram of a decoder in accordance withimplementations of this disclosure.

FIG. 6 is a block diagram of a representation of a portion of a frame inaccordance with implementations of this disclosure.

FIG. 7 is a flowchart diagram of an example of decoding using alphachannel prediction in accordance with implementations of this disclosure

FIG. 8 is a flowchart diagram of an example of encoding using alphachannel prediction in accordance with implementations of thisdisclosure.

DETAILED DESCRIPTION

Image and video compression schemes may include breaking an image, orframe, into smaller portions, such as blocks, and generating an outputbitstream using techniques to minimize the bandwidth utilization of theinformation included for each block in the output. In someimplementations, the information included for each block in the outputmay be limited by reducing spatial redundancy, reducing temporalredundancy, or a combination thereof. For example, temporal or spatialredundancies may be reduced by predicting a frame, or a portion thereof,based on information available to both the encoder and decoder, andincluding information representing a difference, or residual, betweenthe predicted frame and the original frame in the encoded bitstream. Theresidual information may be further compressed by transforming theresidual information into transform coefficients, quantizing thetransform coefficients, and entropy coding the quantized transformcoefficients. Other coding information, such as motion information, maybe included in the encoded bitstream, which may include transmittingdifferential information based on predictions of the encodinginformation, which may be entropy coded to further reduce thecorresponding bandwidth utilization. An encoded bitstream can be decodedto reconstruct the blocks and the source images from the limitedinformation. In some implementations, the accuracy, efficiency, or both,of coding a block using either inter-prediction or intra-prediction maybe limited.

Implementations of coding, such as encoding or decoding, using alphachannel prediction may include using previously reconstructed alphapre-multiplied RGB color values to obtain an alpha channel lower bound,and using the alpha channel lower bound to improve the accuracy of apredictor for the alpha channel.

FIG. 1 is a diagram of a computing device 100 in accordance withimplementations of this disclosure. The computing device 100 shownincludes a memory 110, a processor 120, a user interface (UI) 130, anelectronic communication unit 140, a sensor 150, a power source 160, anda bus 170. As used herein, the term “computing device” includes anyunit, or a combination of units, capable of performing any method, orany portion or portions thereof, disclosed herein.

The computing device 100 may be a stationary computing device, such as apersonal computer (PC), a server, a workstation, a minicomputer, or amainframe computer; or a mobile computing device, such as a mobiletelephone, a personal digital assistant (PDA), a laptop, or a tablet PC.Although shown as a single unit, any one element or elements of thecomputing device 100 can be integrated into any number of separatephysical units. For example, the user interface 130 and processor 120can be integrated in a first physical unit and the memory 110 can beintegrated in a second physical unit.

The memory 110 can include any non-transitory computer-usable orcomputer-readable medium, such as any tangible device that can, forexample, contain, store, communicate, or transport data 112,instructions 114, an operating system 116, or any information associatedtherewith, for use by or in connection with other components of thecomputing device 100. The non-transitory computer-usable orcomputer-readable medium can be, for example, a solid state drive, amemory card, removable media, a read-only memory (ROM), a random-accessmemory (RAM), any type of disk including a hard disk, a floppy disk, anoptical disk, a magnetic or optical card, an application-specificintegrated circuits (ASICs), or any type of non-transitory mediasuitable for storing electronic information, or any combination thereof.

Although shown a single unit, the memory 110 may include multiplephysical units, such as one or more primary memory units, such asrandom-access memory units, one or more secondary data storage units,such as disks, or a combination thereof. For example, the data 112, or aportion thereof, the instructions 114, or a portion thereof, or both,may be stored in a secondary storage unit and may be loaded or otherwisetransferred to a primary storage unit in conjunction with processing therespective data 112, executing the respective instructions 114, or both.In some implementations, the memory 110, or a portion thereof, may beremovable memory.

The data 112 can include information, such as input audio data, encodedaudio data, decoded audio data, or the like. The instructions 114 caninclude directions, such as code, for performing any method, or anyportion or portions thereof, disclosed herein. The instructions 114 canbe realized in hardware, software, or any combination thereof. Forexample, the instructions 114 may be implemented as information storedin the memory 110, such as a computer program, that may be executed bythe processor 120 to perform any of the respective methods, algorithms,aspects, or combinations thereof, as described herein.

Although shown as included in the memory 110, in some implementations,the instructions 114, or a portion thereof, may be implemented as aspecial purpose processor, or circuitry, that can include specializedhardware for carrying out any of the methods, algorithms, aspects, orcombinations thereof, as described herein. Portions of the instructions114 can be distributed across multiple processors on the same machine ordifferent machines or across a network such as a local area network, awide area network, the Internet, or a combination thereof.

The processor 120 can include any device or system capable ofmanipulating or processing a digital signal or other electronicinformation now-existing or hereafter developed, including opticalprocessors, quantum processors, molecular processors, or a combinationthereof. For example, the processor 120 can include a special purposeprocessor, a central processing unit (CPU), a digital signal processor(DSP), a plurality of microprocessors, one or more microprocessor inassociation with a DSP core, a controller, a microcontroller, anApplication Specific Integrated Circuit (ASIC), a Field ProgrammableGate Array (FPGA), a programmable logic array, programmable logiccontroller, microcode, firmware, any type of integrated circuit (IC), astate machine, or any combination thereof. As used herein, the term“processor” includes a single processor or multiple processors.

The user interface 130 can include any unit capable of interfacing witha user, such as a virtual or physical keypad, a touchpad, a display, atouch display, a speaker, a microphone, a video camera, a sensor, or anycombination thereof. For example, the user interface 130 may be anaudio-visual display device, and the computing device 100 may presentaudio, such as decoded audio, using the user interface 130 audio-visualdisplay device, such as in conjunction with displaying video, such asdecoded video. Although shown as a single unit, the user interface 130may include one or more physical units. For example, the user interface130 may include an audio interface for performing audio communicationwith a user, and a touch display for performing visual and touch-basedcommunication with the user.

The electronic communication unit 140 can transmit, receive, or transmitand receive signals via a wired or wireless electronic communicationmedium 180, such as a radio frequency (RF) communication medium, anultraviolet (UV) communication medium, a visible light communicationmedium, a fiber optic communication medium, a wireline communicationmedium, or a combination thereof. For example, as shown, the electroniccommunication unit 140 is operatively connected to an electroniccommunication interface 142, such as an antenna, configured tocommunicate via wireless signals.

Although the electronic communication interface 142 is shown as awireless antenna in FIG. 1, the electronic communication interface 142can be a wireless antenna, as shown, a wired communication port, such asan Ethernet port, an infrared port, a serial port, or any other wired orwireless unit capable of interfacing with a wired or wireless electroniccommunication medium 180. Although FIG. 1 shows a single electroniccommunication unit 140 and a single electronic communication interface142, any number of electronic communication units and any number ofelectronic communication interfaces can be used.

The sensor 150 may include, for example, an audio-sensing device, avisible light-sensing device, a motion sensing device, or a combinationthereof. For example, 100the sensor 150 may include a sound-sensingdevice, such as a microphone, or any other sound-sensing device nowexisting or hereafter developed that can sense sounds in the proximityof the computing device 100, such as speech or other utterances, made bya user operating the computing device 100. In another example, thesensor 150 may include a camera, or any other image-sensing device nowexisting or hereafter developed that can sense an image such as theimage of a user operating the computing device. Although a single sensor150 is shown, the computing device 100 may include a number of sensors150. For example, the computing device 100 may include a first cameraoriented with a field of view directed toward a user of the computingdevice 100 and a second camera oriented with a field of view directedaway from the user of the computing device 100.

The power source 160 can be any suitable device for powering thecomputing device 100. For example, the power source 160 can include awired external power source interface; one or more dry cell batteries,such as nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel metal hydride(NiMH), lithium-ion (Li-ion); solar cells; fuel cells; or any otherdevice capable of powering the computing device 100. Although a singlepower source 160 is shown in FIG. 1, the computing device 100 mayinclude multiple power sources 160, such as a battery and a wiredexternal power source interface.

Although shown as separate units, the electronic communication unit 140,the electronic communication interface 142, the user interface 130, thepower source 160, or portions thereof, may be configured as a combinedunit. For example, the electronic communication unit 140, the electroniccommunication interface 142, the user interface 130, and the powersource 160 may be implemented as a communications port capable ofinterfacing with an external display device, providing communications,power, or both.

One or more of the memory 110, the processor 120, the user interface130, the electronic communication unit 140, the sensor 150, or the powersource 160, may be operatively coupled via a bus 170. Although a singlebus 170 is shown in FIG. 1, a computing device 100 may include multiplebuses. For example, the memory 110, the processor 120, the userinterface 130, the electronic communication unit 140, the sensor 150,and the bus 170 may receive power from the power source 160 via the bus170. In another example, the memory 110, the processor 120, the userinterface 130, the electronic communication unit 140, the sensor 150,the power source 160, or a combination thereof, may communicate data,such as by sending and receiving electronic signals, via the bus 170.

Although not shown separately in FIG. 1, one or more of the processor120, the user interface 130, the electronic communication unit 140, thesensor 150, or the power source 160 may include internal memory, such asan internal buffer or register. For example, the processor 120 mayinclude internal memory (not shown) and may read data 112 from thememory 110 into the internal memory (not shown) for processing.

Although shown as separate elements, the memory 110, the processor 120,the user interface 130, the electronic communication unit 140, thesensor 150, the power source 160, and the bus 170, or any combinationthereof can be integrated in one or more electronic units, circuits, orchips.

FIG. 2 is a diagram of a computing and communications system 200 inaccordance with implementations of this disclosure. The computing andcommunications system 200 shown includes computing and communicationdevices 100A, 100B, 100C, access points 210A, 210B, and a network 220.For example, the computing and communication system 200 can be amultiple access system that provides communication, such as voice,audio, data, video, messaging, broadcast, or a combination thereof, toone or more wired or wireless communicating devices, such as thecomputing and communication devices 100A, 100B, 100C. Although, forsimplicity, FIG. 2 shows three computing and communication devices 100A,100B, 100C, two access points 210A, 210B, and one network 220, anynumber of computing and communication devices, access points, andnetworks can be used.

A computing and communication device 100A, 100B, 100C can be, forexample, a computing device, such as the computing device 100 shown inFIG. 1. For example, the computing and communication devices 100A, 100Bmay be user devices, such as a mobile computing device, a laptop, a thinclient, or a smartphone, and the computing and communication device 100Cmay be a server, such as a mainframe or a cluster. Although thecomputing and communication device 100A and the computing andcommunication device 100B are described as user devices, and thecomputing and communication device 100C is described as a server, anycomputing and communication device may perform some or all of thefunctions of a server, some or all of the functions of a user device, orsome or all of the functions of a server and a user device. For example,the server computing and communication device 100C may receive, encode,process, store, transmit, or a combination thereof audio data and one orboth of the computing and communication device 100A and the computingand communication device 100B may receive, decode, process, store,present, or a combination thereof the audio data.

Each computing and communication device 100A, 100B, 100C, which mayinclude a user equipment (UE), a mobile station, a fixed or mobilesubscriber unit, a cellular telephone, a personal computer, a tabletcomputer, a server, consumer electronics, or any similar device, can beconfigured to perform wired or wireless communication, such as via thenetwork 220. For example, the computing and communication devices 100A,100B, 100C can be configured to transmit or receive wired or wirelesscommunication signals. Although each computing and communication device100A, 100B, 100C is shown as a single unit, a computing andcommunication device can include any number of interconnected elements.

Each access point 210A, 210B can be any type of device configured tocommunicate with a computing and communication device 100A, 100B, 100C,a network 220, or both via wired or wireless communication links 180A,180B, 180C. For example, an access point 210A, 210B can include a basestation, a base transceiver station (BTS), a Node-B, an enhanced Node-B(eNode-B), a Home Node-B (HNode-B), a wireless router, a wired router, ahub, a relay, a switch, or any similar wired or wireless device.Although each access point 210A, 210B is shown as a single unit, anaccess point can include any number of interconnected elements.

The network 220 can be any type of network configured to provideservices, such as voice, data, applications, voice over internetprotocol (VoIP), or any other communications protocol or combination ofcommunications protocols, over a wired or wireless communication link.For example, the network 220 can be a local area network (LAN), widearea network (WAN), virtual private network (VPN), a mobile or cellulartelephone network, the Internet, or any other means of electroniccommunication. The network can use a communication protocol, such as thetransmission control protocol (TCP), the user datagram protocol (UDP),the internet protocol (IP), the real-time transport protocol (RTP) theHyperText Transport Protocol (HTTP), or a combination thereof.

The computing and communication devices 100A, 100B, 100C can communicatewith each other via the network 220 using one or more a wired orwireless communication links, or via a combination of wired and wirelesscommunication links. For example, as shown the computing andcommunication devices 100A, 100B can communicate via wirelesscommunication links 180A, 180B, and computing and communication device100C can communicate via a wired communication link 180C. Any of thecomputing and communication devices 100A, 100B, 100C may communicateusing any wired or wireless communication link, or links. For example, afirst computing and communication device 100A can communicate via afirst access point 210A using a first type of communication link, asecond computing and communication device 100B can communicate via asecond access point 210B using a second type of communication link, anda third computing and communication device 100C can communicate via athird access point (not shown) using a third type of communication link.Similarly, the access points 210A, 210B can communicate with the network220 via one or more types of wired or wireless communication links 230A,230B. Although FIG. 2 shows the computing and communication devices100A, 100B, 100C in communication via the network 220, the computing andcommunication devices 100A, 100B, 100C can communicate with each othervia any number of communication links, such as a direct wired orwireless communication link.

In some implementations, communications between one or more of thecomputing and communication device 100A, 100B, 100C may omitcommunicating via the network 220 and may include transferring data viaanother medium (not shown), such as a data storage device. For example,the server computing and communication device 100C may store audio data,such as encoded audio data, in a data storage device, such as a portabledata storage unit, and one or both of the computing and communicationdevice 100A or the computing and communication device 100B may access,read, or retrieve the stored audio data from the data storage unit, suchas by physically disconnecting the data storage device from the servercomputing and communication device 100C and physically connecting thedata storage device to the computing and communication device 100A orthe computing and communication device 100B.

Other implementations of the computing and communications system 200 arepossible. For example, in an implementation, the network 220 can be anad-hoc network and can omit one or more of the access points 210A, 210B.The computing and communications system 200 may include devices, units,or elements not shown in FIG. 2. For example, the computing andcommunications system 200 may include many more communicating devices,networks, and access points.

FIG. 3 is a diagram of a video stream 300 for use in encoding anddecoding in accordance with implementations of this disclosure. A videostream 300, such as a video stream captured by a video camera or a videostream generated by a computing device, may include a video sequence310. The video sequence 310 may include a sequence of adjacent frames320. Although three adjacent frames 320 are shown, the video sequence310 can include any number of adjacent frames 320.

Each frame 330 from the adjacent frames 320 may represent a single imagefrom the video stream. Although not shown in FIG. 3, a frame 330 mayinclude one or more segments, tiles, or planes, which may be coded, orotherwise processed, independently, such as in parallel. A frame 330 mayinclude one or more tiles 340. Each of the tiles 340 may be arectangular region of the frame that can be coded independently. Each ofthe tiles 340 may include respective blocks 350. Although not shown inFIG. 3, a block can include pixels. For example, a block can include a16×16 group of pixels, an 8×8 group of pixels, an 8×16 group of pixels,or any other group of pixels. Unless otherwise indicated herein, theterm ‘block’ can include a superblock, a macroblock, a segment, a slice,or any other portion of a frame. A frame, a block, a pixel, or acombination thereof can include display information, such as luminanceinformation, chrominance information, or any other information that canbe used to store, modify, communicate, or display the video stream or aportion thereof.

FIG. 4 is a block diagram of an encoder 400 in accordance withimplementations of this disclosure. Encoder 400 can be implemented in adevice, such as the computing device 100 shown in FIG. 1 or thecomputing and communication devices 100A, 100B, 100C shown in FIG. 2,as, for example, a computer software program stored in a data storageunit, such as the memory 110 shown in FIG. 1. The computer softwareprogram can include machine instructions that may be executed by aprocessor, such as the processor 120 shown in FIG. 1, and may cause thedevice to encode video data as described herein. The encoder 400 can beimplemented as specialized hardware included, for example, in computingdevice 100.

The encoder 400 can encode an input video stream 402, such as the videostream 300 shown in FIG. 3, to generate an encoded (compressed)bitstream 404. In some implementations, the encoder 400 may include aforward path for generating the compressed bitstream 404. The forwardpath may include an intra/inter prediction unit 410, a transform unit420, a quantization unit 430, an entropy encoding unit 440, or anycombination thereof. In some implementations, the encoder 400 mayinclude a reconstruction path (indicated by the broken connection lines)to reconstruct a frame for encoding of further blocks. Thereconstruction path may include a dequantization unit 450, an inversetransform unit 460, a reconstruction unit 470, a filtering unit 480, orany combination thereof. Other structural variations of the encoder 400can be used to encode the video stream 402.

For encoding the video stream 402, each frame within the video stream402 can be processed in units of blocks. Thus, a current block may beidentified from the blocks in a frame, and the current block may beencoded.

At the intra/inter prediction unit 410, the current block can be encodedusing either intra-frame prediction, which may be within a single frame,or inter-frame prediction, which may be from frame to frame.Intra-prediction may include generating a prediction block from samplesin the current frame that have been previously encoded andreconstructed. Inter-prediction may include generating a predictionblock from samples in one or more previously constructed referenceframes. Generating a prediction block for a current block in a currentframe may include performing motion estimation to generate a motionvector indicating an appropriate reference portion of the referenceframe.

The intra/inter prediction unit 410 may subtract the prediction blockfrom the current block (raw block) to produce a residual block. Thetransform unit 420 may perform a block-based transform, which mayinclude transforming the residual block into transform coefficients in,for example, the frequency domain. Examples of block-based transformsinclude the Karhunen-Loève Transform (KLT), the Discrete CosineTransform (DCT), the Singular Value Decomposition Transform (SVD), andthe Asymmetric Discrete Sine Transform (ADST). In an example, the DCTmay include transforming a block into the frequency domain. The DCT mayinclude using transform coefficient values based on spatial frequency,with the lowest frequency (i.e. DC) coefficient at the top-left of thematrix and the highest frequency coefficient at the bottom-right of thematrix.

The quantization unit 430 may convert the transform coefficients intodiscrete quantum values, which may be referred to as quantized transformcoefficients or quantization levels. The quantized transformcoefficients can be entropy encoded by the entropy encoding unit 440 toproduce entropy-encoded coefficients. Entropy encoding can include usinga probability distribution metric. The entropy-encoded coefficients andinformation used to decode the block, which may include the type ofprediction used, motion vectors, and quantizer values, can be output tothe compressed bitstream 404. The compressed bitstream 404 can beformatted using various techniques, such as run-length encoding (RLE)and zero-run coding.

The reconstruction path can be used to maintain reference framesynchronization between the encoder 400 and a corresponding decoder,such as the decoder 500 shown in FIG. 5. The reconstruction path may besimilar to the decoding process discussed below and may include decodingthe encoded frame, or a portion thereof, which may include decoding anencoded block, which may include dequantizing the quantized transformcoefficients at the dequantization unit 450 and inverse transforming thedequantized transform coefficients at the inverse transform unit 460 toproduce a derivative residual block. The reconstruction unit 470 may addthe prediction block generated by the intra/inter prediction unit 410 tothe derivative residual block to create a decoded block. The filteringunit 480 can be applied to the decoded block to generate a reconstructedblock, which may reduce distortion, such as blocking artifacts. Althoughone filtering unit 480 is shown in FIG. 4, filtering the decoded blockmay include loop filtering, deblocking filtering, or other types offiltering or combinations of types of filtering. The reconstructed blockmay be stored or otherwise made accessible as a reconstructed block,which may be a portion of a reference frame, for encoding anotherportion of the current frame, another frame, or both, as indicated bythe broken line at 482. Coding information, such as deblocking thresholdindex values, for the frame may be encoded, included in the compressedbitstream 404, or both, as indicated by the broken line at 484.

Other variations of the encoder 400 can be used to encode the compressedbitstream 404. For example, a non-transform-based encoder 400 canquantize the residual block directly without the transform unit 420. Insome implementations, the quantization unit 430 and the dequantizationunit 450 may be combined into a single unit.

FIG. 5 is a block diagram of a decoder 500 in accordance withimplementations of this disclosure. The decoder 500 can be implementedin a device, such as the computing device 100 shown in FIG. 1 or thecomputing and communication devices 100A, 100B, 100C shown in FIG. 2,as, for example, a computer software program stored in a data storageunit, such as the memory 110 shown in FIG. 1. The computer softwareprogram can include machine instructions that may be executed by aprocessor, such as the processor 120 shown in FIG. 1, and may cause thedevice to decode video data as described herein. The decoder 500 can beimplemented as specialized hardware included, for example, in computingdevice 100.

The decoder 500 may receive a compressed bitstream 502, such as thecompressed bitstream 404 shown in FIG. 4, and may decode the compressedbitstream 502 to generate an output video stream 504. The decoder 500may include an entropy decoding unit 510, a dequantization unit 520, aninverse transform unit 530, an intra/inter prediction unit 540, areconstruction unit 550, a filtering unit 560, or any combinationthereof. Other structural variations of the decoder 500 can be used todecode the compressed bitstream 502.

The entropy decoding unit 510 may decode data elements within thecompressed bitstream 502 using, for example, Context Adaptive BinaryArithmetic Decoding, to produce a set of quantized transformcoefficients. The dequantization unit 520 can dequantize the quantizedtransform coefficients, and the inverse transform unit 530 can inversetransform the dequantized transform coefficients to produce a derivativeresidual block, which may correspond to the derivative residual blockgenerated by the inverse transform unit 460 shown in FIG. 4. Usingheader information decoded from the compressed bitstream 502, theintra/inter prediction unit 540 may generate a prediction blockcorresponding to the prediction block created in the encoder 400. At thereconstruction unit 550, the prediction block can be added to thederivative residual block to create a decoded block. The filtering unit560 can be applied to the decoded block to reduce artifacts, such asblocking artifacts, which may include loop filtering, deblockingfiltering, or other types of filtering or combinations of types offiltering, and which may include generating a reconstructed block, whichmay be output as the output video stream 504.

Other variations of the decoder 500 can be used to decode the compressedbitstream 502. For example, the decoder 500 can produce the output videostream 504 without a deblocking filtering unit.

FIG. 6 is a block diagram of a representation of a portion 600 of aframe, such as the frame 330 shown in FIG. 3, in accordance withimplementations of this disclosure. As shown, the portion 600 of theframe includes four 64×64 blocks 610, in two rows and two columns in amatrix or Cartesian plane. In some implementations, a 64×64 block may bea maximum coding unit, N=64. Each 64×64 block may include four 32×32blocks 620. Each 32×32 block may include four 16×16 blocks 630. Each16×16 block may include four 8×8 blocks 640. Each 8×8 block 640 mayinclude four 4×4 blocks 650. Each 4×4 block 650 may include 16 pixels,which may be represented in four rows and four columns in eachrespective block in the Cartesian plane or matrix. The pixels mayinclude information representing an image captured in the frame, such asluminance information, color information, and location information. Insome implementations, a block, such as a 16×16 pixel block as shown, mayinclude a luminance block 660, which may include luminance pixels 662;and two chrominance blocks 670, 680, such as a U or Cb chrominance block670, and a V or Cr chrominance block 680. The chrominance blocks 670,680 may include chrominance pixels 690. For example, the luminance block660 may include 16×16 luminance pixels 662 and each chrominance block670, 680 may include 8×8 chrominance pixels 690 as shown. Although onearrangement of blocks is shown, any arrangement may be used. AlthoughFIG. 6 shows N×N blocks, in some implementations, N×M blocks may beused. For example, 32×64 blocks, 64×32 blocks, 16×32 blocks, 32×16blocks, or any other size blocks may be used. In some implementations,N×2N blocks, 2N×N blocks, or a combination thereof may be used.

In some implementations, video coding may include ordered block-levelcoding. Ordered block-level coding may include coding blocks of a framein an order, such as raster-scan order, wherein blocks may be identifiedand processed starting with a block in the upper left corner of theframe, or portion of the frame, and proceeding along rows from left toright and from the top row to the bottom row, identifying each block inturn for processing. For example, the 64×64 block in the top row andleft column of a frame may be the first block coded and the 64×64 blockimmediately to the right of the first block may be the second blockcoded. The second row from the top may be the second row coded, suchthat the 64×64 block in the left column of the second row may be codedafter the 64×64 block in the rightmost column of the first row.

In some implementations, coding a block may include using quad-treecoding, which may include coding smaller block units within a block inraster-scan order. For example, the 64×64 block shown in the bottom leftcorner of the portion of the frame shown in FIG. 6, may be coded usingquad-tree coding wherein the top left 32×32 block may be coded, then thetop right 32×32 block may be coded, then the bottom left 32×32 block maybe coded, and then the bottom right 32×32 block may be coded. Each 32×32block may be coded using quad-tree coding wherein the top left 16×16block may be coded, then the top right 16×16 block may be coded, thenthe bottom left 16×16 block may be coded, and then the bottom right16×16 block may be coded. Each 16×16 block may be coded using quad-treecoding wherein the top left 8×8 block may be coded, then the top right8×8 block may be coded, then the bottom left 8×8 block may be coded, andthen the bottom right 8×8 block may be coded. Each 8×8 block may becoded using quad-tree coding wherein the top left 4×4 block may becoded, then the top right 4×4 block may be coded, then the bottom left4×4 block may be coded, and then the bottom right 4×4 block may becoded. In some implementations, 8×8 blocks may be omitted for a 16×16block, and the 16×16 block may be coded using quad-tree coding whereinthe top left 4×4 block may be coded, then the other 4×4 blocks in the16×16 block may be coded in raster-scan order.

In some implementations, video coding may include compressing theinformation included in an original, or input, frame by, for example,omitting some of the information in the original frame from acorresponding encoded frame. For example, coding may include reducingspectral redundancy, reducing spatial redundancy, reducing temporalredundancy, or a combination thereof.

In some implementations, reducing spectral redundancy may include usinga color model based on a luminance component (Y) and two chrominancecomponents (U and V or Cb and Cr), which may be referred to as the YUVor YCbCr color model, or color space. Using the YUV color model mayinclude using a relatively large amount of information to represent theluminance component of a portion of a frame and using a relatively smallamount of information to represent each corresponding chrominancecomponent for the portion of the frame. For example, a portion of aframe may be represented by a high-resolution luminance component, whichmay include a 16×16 block of pixels, and by two lower resolutionchrominance components, each of which represents the portion of theframe as an 8×8 block of pixels. A pixel may indicate a value, forexample, a value in the range from 0 to 255, and may be stored ortransmitted using, for example, eight bits. Although this disclosure isdescribed in reference to the YUV color model, any color model may beused.

In some implementations, reducing spatial redundancy may includetransforming a block into the frequency domain using, for example, adiscrete cosine transform (DCT). For example, a unit of an encoder, suchas the transform unit 420 shown in FIG. 4, may perform a DCT usingtransform coefficient values based on spatial frequency.

In some implementations, reducing temporal redundancy may include usingsimilarities between frames to encode a frame using a relatively smallamount of data based on one or more reference frames, which may bepreviously encoded, decoded, and reconstructed frames of the videostream. For example, a block or pixel of a current frame may be similarto a spatially corresponding block or pixel of a reference frame. Insome implementations, a block or pixel of a current frame may be similarto block or pixel of a reference frame at a different spatial locationand reducing temporal redundancy may include generating motioninformation indicating the spatial difference, or translation, betweenthe location of the block or pixel in the current frame andcorresponding location of the block or pixel in the reference frame.

In some implementations, reducing temporal redundancy may includeidentifying a portion of a reference frame that corresponds to a currentblock or pixel of a current frame. For example, a reference frame, or aportion of a reference frame, which may be stored in memory, may besearched to identify a portion for generating a prediction to use forencoding a current block or pixel of the current frame with maximalefficiency. For example, the search may identify a portion of thereference frame for which the difference in pixel values between thecurrent block and a prediction block generated based on the portion ofthe reference frame is minimized and may be referred to as motionsearching. In some implementations, the portion of the reference framesearched may be limited. For example, the portion of the reference framesearched, which may be referred to as the search area, may include alimited number of rows of the reference frame. In an example,identifying the portion of the reference frame for generating aprediction may include calculating a cost function, such as a sum ofabsolute differences (SAD), between the pixels of portions of the searcharea and the pixels of the current block.

In some implementations, the spatial difference between the location ofthe portion of the reference frame for generating a prediction in thereference frame and the current block in the current frame may berepresented as a motion vector. The difference in pixel values betweenthe prediction block and the current block may be referred to asdifferential data, residual data, a prediction error, or as a residualblock. In some implementations, generating motion vectors may bereferred to as motion estimation, and a pixel of a current block may beindicated based on location using Cartesian coordinates as f_(x,y).Similarly, a pixel of the search area of the reference frame may beindicated based on location using Cartesian coordinates as r_(x,y). Amotion vector (MV) for the current block may be determined based on, forexample, a SAD between the pixels of the current frame and thecorresponding pixels of the reference frame.

Although described herein with reference to matrix or Cartesianrepresentation of a frame for clarity, a frame may be stored,transmitted, processed, or any combination thereof, in any datastructure such that pixel values may be efficiently represented for aframe or image. For example, a frame may be stored, transmitted,processed, or any combination thereof, in a two-dimensional datastructure such as a matrix as shown, or in a one-dimensional datastructure, such as a vector array. In an implementation, arepresentation of the frame, such as a two-dimensional representation asshown, may correspond to a physical location in a rendering of the frameas an image. For example, a location in the top left corner of a blockin the top left corner of the frame may correspond with a physicallocation in the top left corner of a rendering of the frame as an image.

In some implementations, block-based coding efficiency may be improvedby partitioning input blocks into one or more prediction partitions,which may be rectangular, including square, partitions for predictioncoding. In some implementations, video coding using predictionpartitioning may include selecting a prediction partitioning scheme fromamong multiple candidate prediction partitioning schemes. For example,in some implementations, candidate prediction partitioning schemes for a64×64 coding unit may include rectangular size prediction partitionsranging in sizes from 4×4 to 64×64, such as 4×4, 4×8, 8×4, 8×8, 8×16,16×8, 16×16, 16×32, 32×16, 32×32, 32×64, 64×32, or 64×64. In someimplementations, video coding using prediction partitioning may includea full prediction partition search, which may include selecting aprediction partitioning scheme by encoding the coding unit using eachavailable candidate prediction partitioning scheme and selecting thebest scheme, such as the scheme that produces the least rate-distortionerror.

In some implementations, encoding a video frame may include identifyinga prediction partitioning scheme for encoding a current block, such asblock 610. In some implementations, identifying a predictionpartitioning scheme may include determining whether to encode the blockas a single prediction partition of maximum coding unit size, which maybe 64×64 as shown, or to partition the block into multiple predictionpartitions, which may correspond with the sub-blocks, such as the 32×32blocks 620 the 16×16 blocks 630, or the 8×8 blocks 640, as shown, andmay include determining whether to partition into one or more smallerprediction partitions. For example, a 64×64 block may be partitionedinto four 32×32 prediction partitions. Three of the four 32×32prediction partitions may be encoded as 32×32 prediction partitions andthe fourth 32×32 prediction partition may be further partitioned intofour 16×16 prediction partitions. Three of the four 16×16 predictionpartitions may be encoded as 16×16 prediction partitions and the fourth16×16 prediction partition may be further partitioned into four 8×8prediction partitions, each of which may be encoded as an 8×8 predictionpartition. In some implementations, identifying the predictionpartitioning scheme may include using a prediction partitioning decisiontree.

In some implementations, video coding for a current block may includeidentifying an optimal prediction coding mode from multiple candidateprediction coding modes, which may provide flexibility in handling videosignals with various statistical properties and may improve thecompression efficiency. For example, a video coder may evaluate eachcandidate prediction coding mode to identify the optimal predictioncoding mode, which may be, for example, the prediction coding mode thatminimizes an error metric, such as a rate-distortion cost, for thecurrent block. In some implementations, the complexity of searching thecandidate prediction coding modes may be reduced by limiting the set ofavailable candidate prediction coding modes based on similaritiesbetween the current block and a corresponding prediction block. In someimplementations, the complexity of searching each candidate predictioncoding mode may be reduced by performing a directed refinement modesearch. For example, metrics may be generated for a limited set ofcandidate block sizes, such as 16×16, 8×8, and 4×4, the error metricassociated with each block size may be in descending order, andadditional candidate block sizes, such as 4×8 and 8×4 block sizes, maybe evaluated.

In some implementations, block-based coding efficiency may be improvedby partitioning a current residual block into one or more transformpartitions, which may be rectangular, including square, partitions fortransform coding. In some implementations, video coding using transformpartitioning may include selecting a uniform transform partitioningscheme. For example, a current residual block, such as block 610, may bea 64×64 block and may be transformed without partitioning using a 64×64transform.

Although not expressly shown in FIG. 6, a residual block may betransform partitioned using a uniform transform partitioning scheme. Forexample, a 64×64 residual block may be transform partitioned using auniform transform partitioning scheme including four 32×32 transformblocks, using a uniform transform partitioning scheme including sixteen16×16 transform blocks, using a uniform transform partitioning schemeincluding sixty-four 8×8 transform blocks, or using a uniform transformpartitioning scheme including 256 4×4 transform blocks.

In some implementations, video coding using transform partitioning mayinclude identifying multiple transform block sizes for a residual blockusing multiform transform partition coding. In some implementations,multiform transform partition coding may include recursively determiningwhether to transform a current block using a current block sizetransform or by partitioning the current block and multiform transformpartition coding each partition. For example, the bottom left block 610shown in FIG. 6 may be a 64×64 residual block, and multiform transformpartition coding may include determining whether to code the current64×64 residual block using a 64×64 transform or to code the 64×64residual block by partitioning the 64×64 residual block into partitions,such as four 32×32 blocks 620, and multiform transform partition codingeach partition. In some implementations, determining whether totransform partition the current block may be based on comparing a costfor encoding the current block using a current block size transform to asum of costs for encoding each partition using partition sizetransforms.

FIG. 7 is a flowchart diagram of an example of decoding using alphachannel prediction 700 in accordance with implementations of thisdisclosure. Decoding using alpha channel prediction 700 may beimplemented in a decoder, such as the decoder 500 shown in FIG. 5.

As shown in FIG. 7, decoding using alpha channel prediction 700 includesidentifying a current pixel at 710, obtaining reconstructed color valuesat 720, obtaining color space converted color values at 730, obtainingan alpha channel lower bound at 740, generating a predicted alphachannel value at 750, obtaining an adjusted predicted alpha channelvalue at 760, generating a reconstructed pixel at 770, and outputting at780.

Although not expressly shown in FIG. 7, decoding using alpha channelprediction 700 may include obtaining, such as receiving via a wired orwireless electronic communication medium, such as the network 220 shownin FIG. 2, or reading from an electronic data storage medium, such asthe memory 110 shown in FIG. 1, at least a portion of an encodedbitstream. Decoding using alpha channel prediction 700 may includegenerating a reconstructed image. Generating the reconstructed image mayinclude generating the reconstructed image using alpha channelprediction.

A current pixel may be identified at 710. The current pixel may be apixel of a current block of the current image, such as block 610 shownin FIG. 6.

Reconstructed color channel values may be obtained for the current pixelat 720. For example, obtaining the reconstructed color channel valuesfor the current pixel may include obtaining the reconstructed colorchannel values for the current pixel expressed in a color model based ona luminance component (Y) and two chrominance components (U and V or Cband Cr), which may be referred to as the YUV or YCbCr color model, orcolor space. Obtaining the reconstructed color channel values mayinclude reconstructing the reconstructed color channel values usingdata, such as residual color channel values, decoded from the encodedbitstream. The data decoded from the encoded bitstream may be losslesslycoded data or lossily coded data.

Color space converted color values for the pixel may be obtained at 730.Obtaining the color space converted color values for the pixel mayinclude color space conversion of the reconstructed color channelvalues, such as from the YUV color space to another color space, such asthe RGB color space, which may include a red color channel (R), a greencolor channel (G), and a blue color channel (B).

An alpha channel lower bound may be obtained at 740. In someimplementations, the alpha channel lower bound may be an approximatealpha channel lower bound, such as in accordance with obtaining thealpha channel lower bound using lossily coded data. Obtaining the alphachannel lower bound may include obtaining a normalized red color channelvalue by dividing the red color channel value (R) by a defined maximumvalue for the red color channel (maxR). Obtaining the alpha channellower bound may include obtaining a normalized green color channel valueby dividing the green color channel value (G) by a defined maximum valuefor the green color channel (maxG). Obtaining the alpha channel lowerbound may include obtaining a normalized blue color channel value bydividing the blue color channel value (B) by a defined maximum value forthe blue color channel (maxB). Obtaining the alpha channel lower boundmay include identifying a maximum value among the normalized red colorchannel value, the normalized green color channel value, and thenormalized blue color channel value, identifying, as the approximatelower bound, a product of multiplying the maximum value among thenormalized red color channel value, the normalized green color channelvalue, and the normalized blue color channel value by a defined maximumvalue for the alpha channel (maxA). Obtaining the alpha channel lowerbound (minA) may be expressed as the following:

${\min\; A} = {{\max\left( {\frac{R}{\max R},\frac{G}{\max G},\frac{B}{\max B}} \right)}*\max\;{A.}}$

In some implementations, normalization may be omitted and obtaining thealpha channel lower bound (minA) may be expressed as minA=max(R, G, B).

A candidate predicted alpha channel value may be generated at 750.Generating the candidate predicted alpha channel value (rpredA) mayinclude using the alpha values of one or more reconstructed contextpixels, such as pixels from blocks above, to the left of, and above andto the left of the current block. For example, the candidate predictedalpha channel value may be an average of the alpha channel values of thecontext pixels.

An adjusted predicted alpha channel value may be obtained at 760 usingthe candidate predicted alpha channel value identified at 750 and thealpha channel lower bound identified at 740. Obtaining the adjustedpredicted alpha channel value (predA) may be expressed aspredA=max(rpredA, minA). In some implementations, obtaining the adjustedpredicted alpha channel value (predA) may include using lossycompression based alpha channel lower bound adjustment (m), such as anadjustment based on the data lost in the lossy compression, such asbased on a quantization level, and obtaining the adjusted predictedalpha channel value (predA) may be expressed as predA=max(rpredA,minA—m).

A reconstructed pixel may be generated at 770. Generating thereconstructed pixel may include generating a reconstructed alpha channelvalue for the pixel. Generating the reconstructed alpha channel valuemay include obtaining, as the reconstructed alpha channel value, a sumof adding the adjusted predicted alpha channel value and a decodedresidual alpha channel value for the pixel.

The reconstructed block pixel may be output at 780. For example, thereconstructed pixel may be included in the reconstructed image, and thereconstructed image may be output, such as via the output stream 504shown in FIG. 5, such as for presentation to a user. Although not shownexpressly in FIG. 7, generating the reconstructed block or thereconstructed image may include filtering, such as the filtering shownat 560 in FIG. 5.

FIG. 8 is a flowchart diagram of an example of encoding using alphachannel prediction 800 in accordance with implementations of thisdisclosure. Encoding using alpha channel prediction 800 may beimplemented in an encoder, such as the encoder 400 shown in FIG. 4.Encoding using alpha channel prediction 800 may be similar to decodingusing alpha channel prediction 700 as shown in FIG. 7, except as isdescribed herein or as is otherwise clear from context.

As shown in FIG. 8, encoding using alpha channel prediction 800 includesidentifying a current pixel at 810, obtaining pre-multiplied colorvalues at 820, obtaining reconstructed color values at 830, obtainingcolor space converted color values at 840, obtaining an alpha channellower bound at 850, generating a predicted alpha channel value at 860,obtaining an adjusted predicted alpha channel value at 860, generating areconstructed pixel at 870, and outputting at 870.

Although not expressly shown in FIG. 8, encoding using alpha channelprediction 800 may include obtaining a current image, which may be aninput image.

A current pixel may be identified at 810. The current pixel may be apixel of a current block of the current image, such as block 610 shownin FIG. 6.

Pre-multiplied color values for the pixel may be obtained at 820. Forexample, the pixel may be expressed in the input image using the RGBcolor space, which may include a red color channel (R), a green colorchannel (G), and a blue color channel (B), and an alpha channel (A).Obtaining the pre-multiplied color values may include identifying aproduct of multiplying the red color channel value by the alpha channelvalue as the pre-multiplied red color channel value, identifying aproduct of multiplying the green color channel value by the alphachannel value as the pre-multiplied green color channel value, andidentifying a product of multiplying the blue color channel value by thealpha channel value as the pre-multiplied blue color channel value.Obtaining the pre-multiplied color values may include obtaining anormalized alpha channel value and using the normalized alpha channelvalue to obtain the pre-multiplied color channel values. Obtaining thenormalized alpha channel value may include obtain a result of dividingthe input alpha channel value by a maximum alpha channel value. In someimplementations, the input color channel values for the pixel may bepre-multiplied color values.

Reconstructed color channel values may be obtained for the current pixelat 830. Although not shown separately in FIG. 8, the pre-multipliedcolor values may be encoded, and the reconstructed color channel valuesmay be obtained by decoding the encoded data for the pre-multipliedcolor values. Encoding the pre-multiplied color values may include colorspace conversion of the pre-multiplied color values, such as from theRGB color space to a color model based on a luminance component (Y) andtwo chrominance components (U and V or Cb and Cr), which may be referredto as the YUV or YCbCr color model, or color space. The encoded data maybe losslessly coded data or lossily coded data.

Color space converted reconstructed color values for the pixel may beobtained at 840. Obtaining the color space converted reconstructed colorvalues for the pixel may include color space conversion of thereconstructed color channel values, such as from the YUV color space toanother color space, such as the RGB color space.

An alpha channel lower bound, which may be an approximate lower bound,may be obtained at 850. Obtaining the alpha channel lower bound mayinclude obtaining a normalized red color channel value by dividing thered color space converted reconstructed color value (R) by a definedmaximum value for the red color channel (maxR). Obtaining the alphachannel lower bound may include obtaining a normalized green colorchannel value by dividing the green color space converted reconstructedcolor value (G) by a defined maximum value for the green color channel(maxG). Obtaining the alpha channel lower bound may include obtaining anormalized blue color channel value by dividing the blue color spaceconverted reconstructed color value (B) by a defined maximum value forthe blue color channel (maxB). Obtaining the alpha channel lower boundmay include identifying a maximum value among the normalized red colorchannel value, the normalized green color channel value, and thenormalized blue color channel value, identifying, as the approximatelower bound, a product of multiplying the maximum value among thenormalized red color channel value, the normalized green color channelvalue, and the normalized blue color channel value by a defined maximumvalue for the alpha channel (maxA). Obtaining the alpha channel lowerbound (minA) may be expressed as the following:

${\min\; A} = {{\max\left( {\frac{R}{\max R},\frac{G}{\max G},\frac{B}{\max B}} \right)}*\max\;{A.}}$

In some implementations, normalization may be omitted and obtaining thealpha channel lower (minA) bound may be expressed as minA=max(R, G, B).

A predicted alpha channel value may be generated at 860. Generating thepredicted alpha channel value (rpredA) may include using the alphavalues of one or more reconstructed context pixels, such as pixels fromblocks above, to the left of, and above and to the left of the currentblock. For example, the predicted alpha channel value may be an averageof the alpha channel values of the context pixels.

An adjusted predicted alpha channel value may be obtained at 870 usingthe candidate predicted alpha value identified at 860 and the alphachannel lower bound identified at 850. Obtaining the adjusted predictedalpha channel value (predA) may be expressed as predA=max(rpredA, minA).

The encoded image data may be output at 880. Although not shownseparately in FIG. 8, the adjusted predicted alpha channel valueobtained at 870 may be subtracted from the input pixel alpha value toobtain a residual alpha value, and the residual alpha value may beincluded in the output bitstream.

In some implementations, the residual alpha value may be an alphachannel lower bound restricted residual alpha value wherein, for aresidual alpha value less than the alpha channel lower bound, the alphachannel lower bound may be used as the residual alpha value. In someimplementations, such as in accordance with obtaining the alpha channellower bound using lossily coded data, an approximation of the alphachannel lower bound wherein, for a residual alpha value less than theapproximation of the alpha channel lower bound, the approximation of thealpha channel lower bound may be used as the residual alpha value. Theapproximation of the alpha channel lower bound may be a product ofmultiplying the alpha channel lower bound by a defined approximationparameter, such as 0.9.

As used herein, the terms “optimal”, “optimized”, “optimization”, orother forms thereof, are relative to a respective context and are notindicative of absolute theoretic optimization unless expressly specifiedherein.

As used herein, the term “set” indicates a distinguishable collection orgrouping of zero or more distinct elements or members that may berepresented as a one-dimensional array or vector, except as expresslydescribed herein or otherwise clear from context.

The words “example” or “exemplary” are used herein to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “example” or “exemplary” not necessarily to be construed aspreferred or advantageous over other aspects or designs. Rather, use ofthe words “example” or “exemplary” is intended to present concepts in aconcrete fashion. As used in this application, the term “or” is intendedto mean an inclusive “or” rather than an exclusive “or”. That is, unlessspecified otherwise, or clear from context, “X includes A or B” isintended to mean any of the natural inclusive permutations. That is, ifX includes A; X includes B; or X includes both A and B, then “X includesA or B” is satisfied under any of the foregoing instances. In addition,the articles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform. Moreover, use of the term “an embodiment” or “one embodiment” or“an implementation” or “one implementation” throughout is not intendedto mean the same embodiment or implementation unless described as such.As used herein, the terms “determine” and “identify”, or any variationsthereof, includes selecting, ascertaining, computing, looking up,receiving, determining, establishing, obtaining, or otherwiseidentifying or determining in any manner whatsoever using one or more ofthe devices shown in FIG. 1.

Further, for simplicity of explanation, although the figures anddescriptions herein may include sequences or series of steps or stages,elements of the methods disclosed herein can occur in various ordersand/or concurrently. Additionally, elements of the methods disclosedherein may occur with other elements not explicitly presented anddescribed herein. Furthermore, one or more elements of the methodsdescribed herein may be omitted from implementations of methods inaccordance with the disclosed subject matter.

The implementations of the transmitting computing and communicationdevice 100A and/or the receiving computing and communication device 100B(and the algorithms, methods, instructions, etc. stored thereon and/orexecuted thereby) can be realized in hardware, software, or anycombination thereof. The hardware can include, for example, computers,intellectual property (IP) cores, application-specific integratedcircuits (ASICs), programmable logic arrays, optical processors,programmable logic controllers, microcode, microcontrollers, servers,microprocessors, digital signal processors or any other suitablecircuit. In the claims, the term “processor” should be understood asencompassing any of the foregoing hardware, either singly or incombination. The terms “signal” and “data” are used interchangeably.Further, portions of the transmitting computing and communication device100A and the receiving computing and communication device 100B do notnecessarily have to be implemented in the same manner.

Further, in one implementation, for example, the transmitting computingand communication device 100A or the receiving computing andcommunication device 100B can be implemented using a computer programthat, when executed, carries out any of the respective methods,algorithms and/or instructions described herein. In addition, oralternatively, for example, a special purpose computer/processor can beutilized which can contain specialized hardware for carrying out any ofthe methods, algorithms, or instructions described herein.

The transmitting computing and communication device 100A and receivingcomputing and communication device 100B can, for example, be implementedon computers in a real-time video system. Alternatively, thetransmitting computing and communication device 100A can be implementedon a server and the receiving computing and communication device 100Bcan be implemented on a device separate from the server, such as ahand-held communications device. In this instance, the transmittingcomputing and communication device 100A can encode content using anencoder 400 into an encoded video signal and transmit the encoded videosignal to the communications device. In turn, the communications devicecan then decode the encoded video signal using a decoder 500.Alternatively, the communications device can decode content storedlocally on the communications device, for example, content that was nottransmitted by the transmitting computing and communication device 100A.Other suitable transmitting computing and communication device 100A andreceiving computing and communication device 100B implementation schemesare available. For example, the receiving computing and communicationdevice 100B can be a generally stationary personal computer rather thana portable communications device and/or a device including an encoder400 may also include a decoder 500.

Further, all or a portion of implementations can take the form of acomputer program product accessible from, for example, a tangiblecomputer-usable or computer-readable medium. A computer-usable orcomputer-readable medium can be any device that can, for example,tangibly contain, store, communicate, or transport the program for useby or in connection with any processor. The medium can be, for example,an electronic, magnetic, optical, electromagnetic, or a semiconductordevice. Other suitable mediums are also available.

It will be appreciated that aspects can be implemented in any convenientform. For example, aspects may be implemented by appropriate computerprograms which may be carried on appropriate carrier media which may betangible carrier media (e.g. disks) or intangible carrier media (e.g.communications signals). Aspects may also be implemented using suitableapparatus which may take the form of programmable computers runningcomputer programs arranged to implement the methods and/or techniquesdisclosed herein. Aspects can be combined such that features describedin the context of one aspect may be implemented in another aspect.

The above-described implementations have been described in order toallow easy understanding of the application are not limiting. On thecontrary, the application covers various modifications and equivalentarrangements included within the scope of the appended claims, whichscope is to be accorded the broadest interpretation so as to encompassall such modifications and equivalent structure as is permitted underthe law.

What is claimed is:
 1. A method, comprising: generating a reconstructedimage, wherein generating the reconstructed image includes generatingthe reconstructed image using alpha channel prediction, whereingenerating the reconstructed image using alpha channel predictionincludes: obtaining reconstructed color channel values for a currentpixel of the current image expressed with reference to first colorspace; obtaining color space converted color channel values for thecurrent pixel by converting the reconstructed color channel values to asecond color space; obtaining an alpha channel lower bound for an alphachannel value for the current pixel using the color space convertedcolor channel values; generating a candidate predicted alpha value forthe current pixel; obtaining an adjusted predicted alpha value for thecurrent pixel using the candidate predicted alpha value and the alphachannel lower bound; generating a reconstructed pixel for the currentpixel using the adjusted predicted alpha value; and including thereconstructed pixel in the reconstructed image; and outputting thereconstructed image.
 2. The method of claim 1, wherein: the first colorspace is the YUV color space; the reconstructed color channel valuesinclude a luminance channel value, a first chrominance channel value,and a second chrominance channel value; and obtaining the reconstructedcolor channel values includes decoding residual color channel valuesfrom an encoded bitstream.
 3. The method of claim 1, wherein the secondcolor space is the RGB color space and the color space converted colorchannel values include a red color channel value, a green color channelvalue, and a blue color channel value.
 4. The method of claim 3, whereinobtaining the alpha channel lower bound includes: obtaining a normalizedred color channel value by dividing the red color channel value by adefined maximum value for the red color channel; obtaining a normalizedgreen color channel value by dividing the green color channel value by adefined maximum value for the green color channel; obtaining anormalized blue color channel value by dividing the blue color channelvalue by a defined maximum value for the blue color channel; identifyinga maximum value among the normalized red color channel value, thenormalized green color channel value, and the normalized blue colorchannel value; and identifying, as the alpha channel lower bound, aproduct of multiplying the maximum value by a defined maximum value forthe alpha channel.
 5. The method of claim 1, wherein generating thecandidate predicted alpha value includes: identifying a previouslyreconstructed context pixel for predicting the candidate predicted alphavalue; and obtaining the candidate predicted alpha value using thepreviously reconstructed context pixel.
 6. The method of claim 1,wherein obtaining the adjusted predicted alpha value includes:identifying, as the adjusted predicted alpha value, a maximum valueamong the candidate predicted alpha value and the alpha channel lowerbound.
 7. A method, comprising: generating an encoded image, whereingenerating the encoded image includes generating the encoded image usingalpha channel prediction, wherein generating the encoded image usingalpha channel prediction includes: identifying a current pixel from aninput image, wherein the current pixel includes input color channelvalues, wherein the input color channel values are expressed withreference to first color space, and wherein the input color channelvalues include an input alpha channel value; obtaining pre-multipliedcolor channel values for the pixel using the input color channel values;obtaining reconstructed color values for the pixel using thepre-multiplied color channel values, wherein the reconstructed colorchannel values are expressed with reference to second color space;obtaining color space converted color channel values for the currentpixel by color space converting the reconstructed color channel valuesto the first color space; obtaining an alpha channel lower bound for areconstructed alpha channel value for the current pixel using the colorspace converted color channel values; generating a candidate predictedalpha value for the current pixel; obtaining an adjusted predicted alphavalue for the current pixel using the candidate predicted alpha valueand the alpha channel lower bound; obtaining a residual alpha value as adifference of subtracting the adjusted predicted alpha value from theinput alpha channel value; and including the residual alpha value in anoutput bitstream; and outputting the output bitstream.
 8. The method ofclaim 7, wherein the second color space is the YUV color space and thereconstructed color channel values include a luminance channel value, afirst chrominance channel value, and a second chrominance channel value.9. The method of claim 7, wherein: the first color space is the RGBcolor space; the input color channel values include an input red colorchannel value, an input green color channel value, and an input bluecolor channel value; the pre-multiplied color channel values include apre-multiplied red color channel value, a pre-multiplied green colorchannel value, and a pre-multiplied blue color channel value; and thecolor space converted color channel values include a color spaceconverted red color channel value, a color space converted green colorchannel value, and a color space converted blue color channel value. 10.The method of claim 9, wherein obtaining the pre-multiplied colorchannel values includes: obtaining, as the pre-multiplied red colorchannel value, a product of multiplying the input red color channelvalue by the input alpha channel value; obtaining, as the pre-multipliedgreen color channel value, a product of multiplying the input greencolor channel value by the input alpha channel value; and obtaining, asthe pre-multiplied blue color channel value, a product of multiplyingthe input blue color channel value by the input alpha channel value. 11.The method of claim 9, wherein obtaining the alpha channel lower boundincludes: obtaining a normalized red color channel value by dividing thecolor space converted red color channel value by a defined maximum valuefor the red color channel; obtaining a normalized green color channelvalue by dividing the color space converted green color channel value bya defined maximum value for the green color channel; obtaining anormalized blue color channel value by dividing the color spaceconverted blue color channel value by a defined maximum value for theblue color channel; identifying a maximum value among the normalized redcolor channel value, the normalized green color channel value, and thenormalized blue color channel value; and identifying, as the alphachannel lower bound, a product of multiplying the maximum value by adefined maximum value for the alpha channel.
 12. The method of claim 7,wherein obtaining the reconstructed color values includes: color spaceconverting the pre-multiplied color channel values to the second colorspace; obtaining respective predicted color channel values for thepre-multiplied color channel values; obtaining respective residual colorchannel values as respective differences of subtracting the predictedcolor channel values from the corresponding pre-multiplied color channelvalues; lossily encoding the residual color channel values to obtainencoded residual color channel values; and obtaining, as thereconstructed color values, respective sums of adding the encodedresidual color channel values to the corresponding predicted colorchannel values.
 13. The method of claim 7, wherein generating thecandidate predicted alpha value includes: identifying a previouslyreconstructed context pixel for predicting the candidate predicted alphavalue; and obtaining the candidate predicted alpha value using thepreviously reconstructed context pixel.
 14. The method of claim 7,wherein obtaining the adjusted predicted alpha value includes:identifying, as the adjusted predicted alpha value, a maximum valueamong the candidate predicted alpha value and the alpha channel lowerbound.
 15. An apparatus, comprising: a processor configured to perform amethod comprising: generating a reconstructed image, wherein generatingthe reconstructed image includes generating the reconstructed imageusing alpha channel prediction, wherein generating the reconstructedimage using alpha channel prediction includes: obtaining reconstructedcolor channel values for a current pixel of the current image expressedwith reference to first color space; obtaining color space convertedcolor channel values for the current pixel by converting thereconstructed color channel values to a second color space; obtaining analpha channel lower bound for an alpha channel value for the currentpixel using the color space converted color channel values; generating acandidate predicted alpha value for the current pixel; obtaining anadjusted predicted alpha value for the current pixel using the candidatepredicted alpha value and the alpha channel lower bound; generating areconstructed pixel for the current pixel using the adjusted predictedalpha value; and including the reconstructed pixel in the reconstructedimage; and outputting the reconstructed image.
 16. The apparatus ofclaim 15, wherein: the first color space is the YUV color space; thereconstructed color channel values include a luminance channel value, afirst chrominance channel value, and a second chrominance channel value;and obtaining the reconstructed color channel values includes decodingresidual color channel values from an encoded bitstream.
 17. Theapparatus of claim 15, wherein the second color space is the RGB colorspace and the color space converted color channel values include a redcolor channel value, a green color channel value, and a blue colorchannel value.
 18. The apparatus of claim 17, wherein obtaining thealpha channel lower bound includes: obtaining a normalized red colorchannel value by dividing the red color channel value by a definedmaximum value for the red color channel; obtaining a normalized greencolor channel value by dividing the green color channel value by adefined maximum value for the green color channel; obtaining anormalized blue color channel value by dividing the blue color channelvalue by a defined maximum value for the blue color channel; identifyinga maximum value among the normalized red color channel value, thenormalized green color channel value, and the normalized blue colorchannel value; and identifying, as the alpha channel lower bound, aproduct of multiplying the maximum value by a defined maximum value forthe alpha channel.
 19. The apparatus of claim 15, wherein generating thecandidate predicted alpha value includes: identifying a previouslyreconstructed context pixel for predicting the candidate predicted alphavalue; and obtaining the candidate predicted alpha value using thepreviously reconstructed context pixel.
 20. The apparatus of claim 15,wherein obtaining the adjusted predicted alpha value includes:identifying, as the adjusted predicted alpha value, a maximum valueamong the candidate predicted alpha value and the alpha channel lowerbound.